Recent advances in the treatment of HIV disease have produced dramatic reductions in AIDS-related morbidity and mortality in the United States. In addition to effective new drug combinations, clinicians now have available a range of improved laboratory monitoring techniques, including HIV RNA, resistance testing, and therapeutic drug monitoring. Yet these advances give rise to new patient-care challenges: How, for example, should medication efficacy be balanced against toxicity in choosing the optimal time to initiate highly active antiretroviral therapy (HAART)? What is the role of structured treatment interruption, both for patients with primary HIV infection and chronic illness? How can resistance testing be used to optimize care? Because these advances in HIV management carry significant cost, both for medications and laboratory technologies, there is also a growing tension between improved clinical outcomes and the increasing costs of care. Over the past seven years, our research team has developed and refined a state-of-the-art computer simulation model of HIV. The "Cost-effectiveness of Preventing AIDS Complications" or "CEPAC" model has been used to address a host of important questions facing HIV-infected persons and their providers. In the first cycle of NIAID support, the team has published 20 papers and presented 18 abstracts at national and international meetings on a broad range of topics, including the cost-effectiveness of antiretroviral therapy, the use of genotypic resistance testing, and optimal regimens for opportunistic infection prophylaxis. In this competing continuation, we propose to further refine and update the CEPAC model to address the most pressing questions in HIV management. We will examine specific hypotheses related to: i) the optimal treatment of primary HIV infection; ii) the timing of HAART initiation; iii) efficient use of genotypic and phenotypic resistance testing, as well as therapeutic drug monitoring; iv) the optimal time to switch a failing HAART regimen; and v) the role of structured treatment interruptions. We will use the model to project life expectancy, costs, and cost-effectiveness of care under a variety of scenarios, and use the results to promote more rational approaches to HIV care by both clinicians and policy-makers.